Minimax and adaptive estimation of general linear functionals under sparsity

We study nonasymptotic minimax estimation of the linear functional $L(θ)=η^\top θ$ for a high-dimensional $s$-sparse mean vector with an arbitrary loading vector $η$. For symmetric noise with exponentially decaying tails, we derive the sharp minimax rate, explicit in $s$, $η$, the tail parameter, an